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Organizational Online Reputation Measurement Through Natural Language Processing and Sentiment Analysis Techniques

dc.contributor.authorOrrego C
dc.contributor.authorVilla L.F
dc.contributor.authorSepúlveda-Cano L.M
dc.contributor.authorGiraldo M L.M.
dc.date.accessioned2022-09-14T14:33:56Z
dc.date.available2022-09-14T14:33:56Z
dc.date.created2021
dc.identifier.isbn9783030867010
dc.identifier.issn18650929
dc.identifier.urihttp://hdl.handle.net/11407/7528
dc.descriptionThe set of perceptions held by various groups based on history and expectations constitutes the reputation of organizations. There are multiple correct measurements of reputation since no general definition of the concept has been reached. ORM (Online Reputation Monitoring-management) systems oversee this measurement and have a sentiment analysis component to perform this task. The literature presents different frameworks or methodologies for measurement developed by academia and industry. These proposals’ common objective is to measure online reputation based on the opinions expressed by individuals close to the organization. In the absence of an automatic ORM system, it is necessary to perform this task manually within a company by a person; this can generate operational errors, delay processes, and make scalability impossible to increase the number of items reviewed (news, comments). These drawbacks can be mitigated by automating the measurement of a client’s online reputation. This paper contains the development of three methodologies from the literature to explore online reputation measurement starting from Twitter and Google News information sources. The implementation results conclude that the POS-Tagger elimination methodology generates the best result compared to the coded methodologies. © 2021, Springer Nature Switzerland AG.eng
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85116802456&doi=10.1007%2f978-3-030-86702-7_6&partnerID=40&md5=38148c472cea388b6416b00a10e68078
dc.sourceCommunications in Computer and Information Science
dc.titleOrganizational Online Reputation Measurement Through Natural Language Processing and Sentiment Analysis Techniques
dc.typeConference Paper
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Sistemas
dc.publisher.programIngeniería de Telecomunicaciones
dc.type.spaDocumento de conferencia
dc.identifier.doi10.1007/978-3-030-86702-7_6
dc.subject.keywordE-reputationeng
dc.subject.keywordOnline reputationeng
dc.subject.keywordReputation assessmenteng
dc.subject.keywordReputation managementeng
dc.subject.keywordReputation measurementeng
dc.subject.keywordSentiment analysiseng
dc.subject.keywordComputational linguisticseng
dc.subject.keywordOnline systemseng
dc.subject.keywordAnalysis techniqueseng
dc.subject.keywordE-reputationeng
dc.subject.keywordManagement systemseng
dc.subject.keywordMeasurements ofeng
dc.subject.keywordOnline reputationeng
dc.subject.keywordOrganisationaleng
dc.subject.keywordReputation assessmentseng
dc.subject.keywordReputation managementeng
dc.subject.keywordReputation measurementeng
dc.subject.keywordSentiment analysiseng
dc.subject.keywordSentiment analysiseng
dc.relation.citationvolume1431 CCIS
dc.relation.citationstartpage60
dc.relation.citationendpage71
dc.publisher.facultyFacultad de Ingenierías
dc.affiliationOrrego, C., Universidad de Medellín, Medellín, Colombia
dc.affiliationVilla, L.F., Universidad de Medellín, Medellín, Colombia
dc.affiliationSepúlveda-Cano, L.M., Universidad de Medellín, Medellín, Colombia
dc.affiliationGiraldo M, L.M., Universidad de Medellín, Medellín, Colombia
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dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/other
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín
dc.relation.ispartofconference8th Workshop on Engineering Applications, WEA 2021


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